Development of the probabilistic neural network-cubic least squares mapping (PNN-LSM3) classifier to assess carotid plaque's risk

  • Authors:

  • N. Piliouras;I. Kalatzis;P. Theocharakis;N. Dimitropoulos;D. Cavouras

  • Affiliations:

  • Department of Medical Instrumentation Technology, Technological Educational Institution of Athens, Ag. Spyridonos Street, Egaleo, GR-122 10 Athens, Greece;Department of Medical Instrumentation Technology, Technological Educational Institution of Athens, Ag. Spyridonos Street, Egaleo, GR-122 10 Athens, Greece;Department of Medical Instrumentation Technology, Technological Educational Institution of Athens, Ag. Spyridonos Street, Egaleo, GR-122 10 Athens, Greece;Department of Medical Imaging, EUROMEDICA Medical Center, 2 Mesogeion Avenue, GR-11527 Athens, Greece;Department of Medical Instrumentation Technology, Technological Educational Institution of Athens, Ag. Spyridonos Street, Egaleo, GR-122 10 Athens, Greece

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2004

Quantified Score

Hi-index 0.10

Visualization

Abstract

An efficient classification algorithm based on the cubic least squares mapping (LSM3) and the probabilistic neural network (PNN) classifier is proposed for assessing the carotid plaque's risk of causing brain infarcts. Ultrasound images of 24 high-risk and 32 low-risk carotid plaques were manually segmented by an experienced physician using a custom developed software. Three textural features, related to the plaque's internal composition, the PNN, and the PNNLSM 3 classification algorithms were used to design a classification system. PNN classification accuracy was 92.9%, misdiagnosing one high-risk and three low-risk plaques while the PNN-LSM3 managed to classify all plaques correctly. The proposed system may be of value to patient management as a second opinion tool, after it is tested on more data in a clinical environment.